In order to solve the problem that the existing illumination estimation methods have low accuracy in image restoration, a random vector functional link illumination estimation algorithm based on whale optimization arithmetic algorithm is proposed in this article. First, in order to improve the optimization accuracy of arithmetic optimization algorithm, the whale optimization algorithm is applied to optimize the initial population of the arithmetic optimization algorithm. Then, we utilize the improved arithmetic optimization algorithm to improve the input weight and hidden layer offset of random vector functional link, and exclude the uncertainty caused by the random generation of random vector functional link's input weight and hidden layer bias. Finally, the proposed whale arithmetic optimization algorithm-random vector functional link model is applied to SFU lab dataset and Gehler-Shi dataset for illumination estimation. Compared with the other earlier illumination estimation algorithms, the proposed illumination estimation model in this article has better effect and higher stability. In addition, due to the diagonal mapping method for color correction and image restoration, the proposed illumination estimation algorithm can also get better results in image restoration.

中文翻译：

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基于改进算法优化算法的随机向量泛函光照估计

针对现有光照估计方法在图像恢复中精度不高的问题，本文提出了一种基于鲸鱼优化算法的随机向量泛函链路光照估计算法。首先，为了提高算术优化算法的优化精度，应用鲸鱼优化算法对算术优化算法的初始种群进行优化。然后，我们利用改进的算术优化算法来改进随机向量函数链接的输入权重和隐藏层偏移，并排除随机生成随机向量函数链接的输入权重和隐藏层偏差所带来的不确定性。最后，将提出的鲸鱼算法优化算法-随机向量函数链接模型应用于SFU实验室数据集和Gehler-Shi数据集进行光照估计。与较早的其他光照估计算法相比，本文提出的光照估计模型具有更好的效果和更高的稳定性。此外，由于采用对角映射方法进行颜色校正和图像恢复，所提出的光照估计算法在图像恢复中也能得到较好的效果。